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Related Experiment Video

Updated: Mar 11, 2026

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Modelling microbial metabolic rewiring during growth in a complex medium.

Marco Fondi1, Emanuele Bosi2, Luana Presta2

  • 1Department of Biology, University of Florence, Via Madonna del Piano 6, I-50019, Sesto F.no, Italy. marco.fondi@unifi.it.

BMC Genomics
|November 25, 2016
PubMed
Summary

Bacteria adapt to changing environments by reprogramming their metabolism. This study shows Pseudoalteromonas haloplanktis TAC125 undergoes significant metabolic shifts, involving over 50% of its genes, to optimize growth in complex media.

Keywords:
Antarctic bacteriaFlux balance analysisMetabolic modellingPseudoalteromonas haloplanktis TAC125

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Area of Science:

  • Microbial physiology
  • Systems biology
  • Metabolic modeling

Background:

  • Bacteria constantly adapt to fluctuating environmental conditions, including nutrient availability.
  • Understanding microbial metabolic network reorganization is crucial but underexplored in many species.

Purpose of the Study:

  • To model the metabolic reprogramming of Pseudoalteromonas haloplanktis TAC125 in a complex medium over time.
  • To investigate the system-level consequences of nutrient switching on microbial growth and metabolism.

Main Methods:

  • Utilized multi-step constraint-based metabolic modeling to simulate bacterial growth.
  • Employed a MOMA-based approach (nutritional-MOMA) to explore sub-optimal growth objectives.
  • Integrated modeling with fed-batch growth data.

Main Results:

  • Predicted significant metabolic reprogramming, involving over 50% of metabolic genes, for optimal growth.
  • Demonstrated the model's ability to capture gene functional associations and co-regulation.
  • Showed that sub-optimal objective functions impact flux distribution predictions compared to standard Flux Balance Analysis (FBA).

Conclusions:

  • Provided a time-resolved, systems-level view of metabolic re-wiring in P. haloplanktis TAC125 due to carbon source switching.
  • Highlighted the bacteria's efficient metabolic reprogramming machinery for adapting to changing nutrient environments.
  • Confirmed that integrating modeling with growth data predicts functional partnerships and co-regulation, and sub-optimal objectives alter metabolic flux predictions.